The GA4 integration with Optimize, is capable of  sending data to and from the platform. It links to your GA4 properties to unlock additional reporting and more importantly, to act on data from audiences’ behavior on your sites.

For marketers who are looking to step up their conversion rate optimization, it provides value by combining user experience and attribution data with personalization and A/B testing. However, I have some bad news and upcoming good news about this GA4 feature.

Now, the good news. A recent announcement has confirmed that something much bigger and better is coming soon. There will be new integrations with Google Analytics 4, including popular platforms Optimizely, VWO, and AB Tasty. It doesn’t stop there. There’s essentially unlimited opportunities for integrations. A public API will be available that will allow any third-party experiment tool to connect to GA4.

This sunset should be considered a great decision by Google because they’re putting personalization in the hands of the most powerful A/B testing tools in the industry. The Google Marketing Platform’s long-standing dominance is with their media tools and GA, not with personalization (if you’re a veteran GA user, this may be bringing back some rough memories of Google Content Experiments). 

Dig deeper: All coverage of Google Analytics 4

Why we care. Optimize is an excellent tool. The interface is intuitive, it has advanced targeting options, real-time reporting, and the integration with Google Analytics. However, it’s a relatively young player in the A/B testing industry (Optimizely and VWO have existed for well over a decade). It works well, but it’s like hiring someone who does this work on the side instead of someone who’s dedicated full time. With this release, you’ll have the analytics power of GA4 combined with the freedom you get by choosing any A/B testing tool.

The new feature will help measure more engagement, higher insight into user behavior, and deeper personalization. GA4 audiences will be able to pass to these platforms where you can target groups of users based on specific content interest, traffic sources, attributes, and any custom information that you’re collecting about your site visitors (non-PII of course).

What you should do. The action to take now is to export any historic reports on experiments in Google Analytics and Optimize so you can ensure that legacy data is accessible to you and your team. Most importantly, another action to take is to prepare for the future by planning strategy, data collection, and conversion tracking that you’ll use in A/B testing tools.

The post Google to remove GA4 integration with Optimize appeared first on MarTech.

Google Analytics now has the ability to combine observed data and unobserved data. Not only is this a benefit, but it’s a necessity as changes in browser cookies and user identifiers increasingly limit the old way of tracking. 

Our tracking and analytics tools are losing data as we know it — and we must adapt. Using some easy features in GA will help compensate for this loss so that you can remain data-informed.

Dig deeper: 3 ‘secret’ marketing tools in Google Analytics 4

Unobserved data: How it works and why it matters 

No matter which analytics tool you use, leveraging unobserved data is a great tool to keep up with the evolving environment of digital marketing analysis. The difference between unobserved and observed data is the difference between collected data and modeled data. 

Tracking users with cookies used to be more reliable since almost all browsers accepted cookies. The way it functions with analytics is by automatically stamping a user with a cookie when they land on a website. This cookie allows platforms like GA to identify users by device information, location, demographics and, most importantly, a random ID that’s “sticky.” 

When that user returns to the website, the ID is recognized by GA as a returning user, which stitches that user’s past information with new activity. For mobile apps, the behavior is similar. Instead of a cookie, devices have a unique advertising ID as an identifier (Android and iOS have different versions.)

However, things have been changing gradually over the past several years and will continue to change. There’s a huge problem with this old behavior: it gave users little to no control over their personal information being shared. Privacy wasn’t a consideration, and organizations had 100% control over their audience’s information. 

No personally identifiable information (PII) was ever tracked with Google Analytics by default as collecting such data to GA is against the terms of service, but the definition of PII has changed depending on how policies are written and interpreted by different laws and security teams. 

Now, users can block and opt out of analytics tools from collecting data. Automatic opt-out is the default for GDPR and other countries’ laws are certain to adopt this. It’s the “cookieless future.” 

Long story short — we won’t get the volume or detail of user data we used to, so it’s time to fill that gap. In Google Analytics 4, there are several out-of-the-box features to make up for lost data. They require little to no lift once tracking has been set up, so you can test and take advantage of them today. Three examples are:

• Data-driven attribution
• Predictive metrics
• Behavior modeling

Dig deeper: What do marketing attribution and predictive analytics tools do?

1. Data-driven attribution

In GA4, data-driven attribution (DDA) may be somewhat hard to find if you’re not familiar with the interface. It’s located in the Advertising screen instead of the Reports area. The Advertising reports are interesting and split out because they provide a different view of your data. 

In Universal Analytics (sometimes referred to as GA3), the closest equivalent is the Multi-Channel Funnel reports. It’s a good descriptor because these reports expand the analysis of conversions into multiple touchpoints and a fuller user journey. Previously, data-driven attribution was only available to paid 360 accounts but now it’s available to all.

The DDA attribution model uses a statistical model to show how significant a channel was in assisting a conversion. For example, there may be 5,000 purchases attributed to the Organic Search channel in the main GA4 acquisition reporting but the previous touchpoints from the Paid Search channel may be significantly influential to the user who ultimately purchases. 

The statistical model will take the data about users’ behavior and paths leading up to the conversion and determine how much credit the different touchpoints should receive. Instead of 100% credit going to organic in the previous example, credit would be divided by percentages across all channels users came from before making a transaction.

The visualization of DDA is located in the Advertising > Conversion Paths report (pictured above.)

2. Predictive metrics

We have data about what users saw and engaged with, but what will they do next? This is the ultimate example of unobserved data because it involves “future” behavior. As a note, this feature currently relates only to ecommerce and churning data.

Ecommerce tracking will need to be set up before predictive metrics and predictive audiences can be used. If you have ecommerce tracking, the top areas to see and use predictive modeling are in the Explore reports and the Audience tool.

In the Explore reports, predictive metrics are best used in the User Lifetime technique. In this report type, you can choose metrics to import based on purchase probability, churn probability and predicted revenue. There’s a section dedicated to those metrics on the selection screen.

The predictive data in GA4 (both here and in the Audience tool) is based on past user activity. With the data points of users who have made a purchase compared to those who haven’t, the model will learn trends that develop the probabilities and percentiles. For churn, the model looks at users who are active and users who become inactive to determine who won’t come back to your site or app in the next week.

The insights can be used outside of Google Analytics as well. Audiences and segments can be created to isolate likely/unlikely purchasers and used in Google Ads for remarketing. To build a predictive audience in a few clicks, you can go to Admin > Audiences > New Audience > Predictive. This will give you pre-made templated audiences to use and customize how you’d like (pictured below.)

3. Behavior modeling

Behavior modeling is the most impactful machine-learning feature out of these three because it affects user tracking right from the source — the identifier. It involves integrating GA4 with your cookie consent management tool so that Google Analytics can collect data on users who don’t consent to be tracked.

This sounds counter-intuitive, but the data is anonymized and not related to a cookie or any user identifier. Instead, the anonymous event-only data is used to determine user-level activity. It’s powerful because it’s based on your site or app’s data. The behavior of observed users (users who opt-in to tracking) trains a machine-learning model to estimate the behavior of users who opt out of tracking.

If you’re interested in taking advantage of behavior modeling, Google’s documentation on consent mode can help start conversations and action around using this user tracking method. The option to enable behavior modeling in your GA4 account is in Admin > Reporting Identity > Blended. 

Making the most out of GA4’s machine-learning features

With the tools above, questions about your users and data can transform from “How many views did page X receive?” to “Which users are most likely to make a large purchase within the next 7 days?” This sophistication is much more actionable. 

Combining GA4’s machine-learning methods with remarketing and audience-sharing can launch your analytics from solely analysis to immediate use cases and even audience engagement and RoAS impact. 

Dig deeper into GA4 with these stories.

The post 3 Google Analytics 4 features to make up for lost data appeared first on MarTech.

The “Admin” screen is usually frequented by analytics practitioners and those implementing data collection, but there are marketing benefits to getting familiar with the features available. 

Exploring Admin capabilities involves the consideration of property settings for account governance, attribution, reporting enhancement and overall user tracking.

Below are three things to know and keep in mind about Google Analytics 4 account governance.

What are properties in GA4?

In Google Analytics, a property is where the reporting on web or app data collection is located. Right now, there are two different types of properties — Google Analytics 4 and the previous version of GA, Universal Analytics (sometimes referred to as “GA3”). The latter version will sunset and stop accepting data by July 2023, so all marketers should be migrated to GA4 or in the midst of migration. 

As a tip, you can determine if it’s the old version by looking at the property ID. The ID is visible under the property name in the main dropdown or in parentheses in the Admin > Property column.

Does the number follow the letters “UA” and look something like this: UA-12345-1? If yes, that means that it is not a GA4 property.

Universal Analytics property IDs are tracking IDs. So, that ID would go on the website or in a tag manager to collect the data.

It’s important to know that the data source should be reviewed or added if you see any missing data or broken tracking. The number to request for GA4 is “measurement ID.”

Properties aren’t just about base measurement and they do much more than house the data and reports in GA4. Property settings and governance can affect remarketing capabilities, data integrity and even interpreting conversions. 

Dig deeper: Getting started with Google Analytics 4 (Tools and resources)

How account structure relates to measurement strategy

Account structure is the foundation of a reliable and actionable analytics implementation. Decisions around where data should flow into which properties affect the analysis of the user journey. This is why it’s important to have an understanding of how properties work with each other. 

When dealing with only one website or app, it’s typically straightforward and only involves creating a single data stream for a single property. For multiple websites and apps, websites with multiple domains and Google Analytics 360 customers, there are more great features to keep in your back pocket.

If a website has different domains (such as www.exampleA.com and www.exampleB.com), it’s essential for marketers to make note of where the data is and why. This is a step where things can go wrong, resulting in a drop in data integrity.

There are three questions to solve with the knowledge of how users are expected to move between sites along with how team members are going to get the most out of GA4. 

1. Will users navigate back and forth between the domains as part of the web experience?

If not, no action is necessary for this part and data can (and should) be in separate properties.

If yes, the domains must be in the same property. If you find yourself navigating to two different properties for reports, what’s called cross-domain tracking needs to be enabled. With GA4, it can be a low level of effort. 

Next steps to do or request: 

• Make sure all data goes to one property with one measurement ID. 
• Check Admin > Property > Data Streams.

• Then, navigate to the data stream mentioned earlier in the article and simply add your domains. The screen should look like this:

Note: This includes PayPal since the user flow will likely come back to your site after payment in most cases.

2. Would it be better and more efficient to incorporate a feature that allows for more granular data available in the account structure? 

(For those who are familiar with Universal Analytics, this could be thought of as a replacement for Views. ***360 paid customers only. Your Google Partner vendor can provide guidance or assistance, if needed.)

Almost all organizations can answer “yes” to this question. Having subsets of data might be useful for:

• Making a different interface for a section of the site and content.
• Splitting by teams so they have their own custom interface.
• Separating conversion settings and Ads Links.

The next step only involves Google Analytics and not technical or implementation work. Subproperty creation is in the same area where regular source properties are (Admin).

3. Is there an opportunity to aggregate data from multiple GA4 properties into a holistic rollup property?

On the other hand, there may be opportunities to take more than one regular property and do the opposite subproperties by rolling them up to a holistic rollup property. 

This enablement just involves creating the rollup property type and selecting which existing properties you’d like to include in the same admin area as the subproperties.

(***360 paid customers only. Your Google Partner vendor can provide guidance or assistance, if needed.)

Property settings can enable more data and activation

Other Admin capabilities are also in the properties area. For media metrics and audiences, integrations are essential. Linking Google Ads to Google Analytics 4 should be one of the first steps in migration or a refreshed measurement strategy. In the same Admin area mentioned above, the integrations are located at the bottom of the property column.

To link, no tracking changes are needed and it is self-serve. The prerequisites are having the Edit role in GA4 and the role of Administrator in Google Ads.

The Ads account will show up on the integration screen and everything is good to go. Audiences created in Google Analytics will be available in Ads automatically.

You will also have to check to make sure that the toggles are enabled for Google Signals and Ads Personalization on the same Admin screen where the other former steps are located (Admin > Data Settings).

If they are off, you may want to double-check with whoever is the administrator of GA (if it’s not you) to make sure that it is okay to use these settings in alignment with your privacy policies.

Make meaningful improvements to your analytics

There will be edge cases to any implementation, but the above topics and questions should kickstart more collaboration between marketers and analytics for meaningful improvements. 

Having data in the right place, in addition to tracking correctly with enablement for media, will open up opportunities for valuable audience creation.

It might seem like many steps. But just a few settings, toggles and reviews of the properties can have a payoff for decision-making and analytics involvement for marketers in Google Analytics 4.

Getting started with Google Analytics 4

Catch up on the entire series:

The post 3 “secret” marketing tools in Google Analytics 4 appeared first on MarTech.

There are features in the new version of GA that allow for actions and quick analyses that weren’t available before. It’s much simpler to report on audiences, and even if a website or app only has basic tracking, there are options to create advanced and in-depth segments based on out-of-the-box metrics. 

The best part? It only takes a few steps, yet the segmentation can have an impact beyond reporting for remarketing and personalization in other tools.

Dig deeper: GA4: What marketers need to know for a successful transition

Why segment in GA4?

Out of hundreds of thousands (or millions) of data points, getting to know your customers is a needle-in-the-haystack situation. The total audience will never be made up of identical users in their behavior, engagement and decision-making. Segments allow you to isolate a subset of data based on user attributes across:

• Demographics.
• Content interest.
• Behavior.
• Traffic. 

Some examples of questions that you can answer with segmentation are:

• When people find the site naturally through organic search, which content areas are they most likely to browse?
• What do the highest spenders engage with while on the site or app?
• Which age range is most likely to sign up for emails and notifications?
• Which personalized experience resonates better with the group of people who have added high-value items to their cart but didn’t check out?
• Are subscribers more active on mobile or the desktop experience?

At its foundation, segmenting is about understanding, planning and targeting.

Using tools in GA4

In GA4, information is in the form of dimensions and metrics. Every measurement is an event, whether it’s a page view, start of a session, video play, lead form, or timer. 

When segmenting your audience, you’ll have to choose what to base the segmentation on. A good start is to evaluate what’s already tracking and what you have to work with in the Lifecycle Reports collection.

You can create a set of rules to design conditions about who and what to include in a particular group. There’s more than one to explore and each has its appropriate use case: 

• Comparisons.
• Explorations.
• Audiences. 

You can think of them as a funnel from broad exploration, to visual analysis, to taking action. This process is an effective way to plan how you will start your segmentation strategy. 

Quick tip. Check out the configuration area for a list of events and dimensions to see what you can potentially leverage for your conditions.

Comparisons

The simplest one and the highest level of exploration is called Comparisons. These additions are available throughout the interface at the top of the screen as you navigate through the reports — from overviews to specific elements like Pages and Screens. If you’ve used Google Analytics in the past, this is similar to the older functionality.

You can add up to five conditions to include or exclude groups from the reports within the slider. That’s not much of a limitation since all the scorecards, visualizations and table rows are replicated for each segment. 

It’s best to add these just a few at a time so the reports are still easily readable. For example, if I’m curious to compare devices and see user retention over time, I’ll start by building a segment with device condition and apply. It’s okay to see the mobile device as part of the whole since it is a comparison to this default All Users segment, but I want first just to see mobile on its own and then a comparison of mobile vs. desktop instead of mobile vs. All Users. 

Though this is a valuable tool, Comparisons aren’t meant to be for main reporting purposes or in-depth insights. They’re temporary and provide a quick way to spot trends and gather ideas for further segmentation with Explorations and Audiences. 

Explorations

Explorations is another option in the data visualization and interactive reporting feature. This reflects the stage of analysis where there are more specific reports to pull and questions to solve. The segment builder has more advanced conditions, and explorations can have up to 10 segments. 

Since Exploration reports are easily shareable, it’s an effective way to collaborate. These custom reports also offer automated anomaly detection that will apply a statistical model to your data and visually show you when there’s unexpected behavior.

Audiences

The most impactful segment tool in Google Analytics 4 is the Audiences feature. It’s more dynamic than Comparisons and Exploration. Because Audiences collect rather than calculate data, so it will only start including users from the time of creation. 

There’s an option for membership and the ability to fire an event when a new user joins (which makes big opportunities for conversions!).

Audiences can even be combined with a method that was previously out of reach for most marketers — predictive analytics. Now, GA4 users can build segments easily in the interface around aspects like “most likely to be a top spender.” Audiences become a dimension of their own, so the segments of users will be available to use in reports in the interface, Data Studio, or any platform you’re using to build visualizations and dashboards with GA4 data. 

Audiences will also automatically be available in the linked Google Ads account and can be used in other Google Marketing Platform integrations, including Google’s A/B testing tool, Optimize.

Wrapping up

Ultimately, getting to know the characteristics and trends of your user groups is one of the essential purposes of Google Analytics. 

By getting familiar with the conditions, the purpose and the use cases of the segmentation tools, you’ll be able to make quicker, focused and informed decisions that affect your organization’s marketing strategy and results.

The post 3 ways to do segmentation in Google Analytics 4 appeared first on MarTech.

Other features and reporting experiences aside, an edge that Google Analytics has over other analytics platforms is that it fits well with the Google Marketing Platform (GMP). If you’re using Google Ads, Search Ads 360, DV360, or other media tools in the suite, GA can be a hub, as well as a source in the media activation process.

GA integrations as a hub

The paid media platforms in GMP have advanced, automated reporting. These platforms are powerful tools to analyze the beginning of the user journey by drawing people to the site and to the end of the experience by converting. 

What about the middle? A solid Google Analytics implementation offers multi-step conversions, custom user behavior data and rich segment data to build and share audiences.

GA integrations as sources for insights

Google Analytics 4 isn’t just about analyzing data, it’s about acting on it. For example, the Audience feature leverages your analytics implementation — you can use the data to segment users and create audiences for remarketing, targeting, A/B testing, and personalization. 

Through settings in GA, you can also link other products and share audience and conversion data.

Below are the integrations currently available for Google Analytics 4 as of June 2022. Notice that it’s already quite a lengthy list.

• Google Ads.
• BigQuery (extra costs are incurred in Google Cloud).
• Display & Video 360 (DV360).
• Google Ad Manager  (GAM).
• Google Merchant Center.
• Google Optimize. 
• Salesforce Marketing Cloud (SFMC) (this one requires the Salesforce Journey Builder). 
• Search Console.
• Play integration.
• Search Ads 360 (SA360).

The first step to building out your analytics insights is taking inventory of your GMP stack. Which products are you using right now? The products will depend on what type of site or app you have and the products in which you are investing. However, three of those integrations can apply to all properties — BigQuery, Search Console and Optimize. It doesn’t matter if you’re an advertiser, publisher, retail or service site — each of these integrations is a possibility to use today for free in Google Analytics 4. 

Let’s take a closer look at these three fundamental integrations.

BigQuery

What is BigQuery? A Google Cloud data warehouse that’s not exclusively for Google Analytics or GMP.

Who is it for? Teams and leaders that will benefit from this connection are involved in areas like BI, data science, and data administration.

With BigQuery, you’ll have all of your data exported to a data warehouse that you own and control. Once the data is in Google Cloud, there’s freedom to send to another database, blend with data outside of Google Analytics, and perform advanced reporting in other tools. The GA BigQuery data has other benefits, including integration with CRM data.

How to integrate. The integration is self-serve within the interface, but there needs to be a BigQuery project available to link the Google Analytics tool. If you do not have a project yet, go to the Google APIs Resources page to create a new one. On the page, it looks technical and there’s code references, but that part isn’t necessary and you can skip it. The instructions for doing it through the interface are in modules in the “Console” tab. Below are the simplified steps:

1. Select the option to create a project on the upper left of the page.

2. Name your project, select the “Create” button, and there’s now a new project in Google Cloud. 
3. The last step is turning on a setting to use BigQuery. There are a lot of technical options in the menu, but the only area you need to go to for this is “Library” under “APIs & Services,” where you can search for BigQuery and enable it.

After the project is created, it’s ready to be integrated with Google Analytics 4. Back in the GA interface, the option to link it is under property settings. 

Now your raw GA4 data will start collecting into the project to be available for immediate use. Out of the integrations listed here, this one has the most steps. However, the other products are just a few clicks. (Note: BigQuery comes at an extra cost. However, for most accounts it will not be significant — it is sometimes just a few dollars.) 

Search Console

What is Search Console? It’s a platform for monitoring in-depth metrics and reports related to organic Google search performance and site speed.

Who is it for? Most teams will benefit in some way from analyzing search data. This includes content creators, SEO teams, and web developers.

How to integrate. A Search Console property must be created, and it must be verified. Sometimes this is as simple as selecting a few buttons in the interface.

Once there is a Search Console property, or once there is access to an existing property, the link is in the same menu as the BigQuery link under Property Settings.

After, organic metrics and reports that are not out-of-the-box will be available in Google Analytics 4. Once the product linking is complete and working, there’s a last step to enable GA users to benefit from the enhanced data. It may be noticeable (and possibly confusing) that the Search Console data isn’t within the default interface navigation. To see the reports, the reporting collections in the menu should be edited.

To modify the navigation, select “Library” at the bottom of the screen:

Next, begin the process to create a collection, under Collections. The template for Search Console will be located as the bottom right option. The option to start from scratch without a template is also available.

After saving, go back to the library area and publish your collection. The report should now be accessible from the left navigation:

Optimize

What is Optimize? Optimize is an A/B testing and personalization tool.

Who is it for? It’s for marketers, conversion rate optimization (CRO) teams, content creators, or UX leads.

How to integrate. This one isn’t as apparent as the other links. Right now, the integration option does not show up in the Google Analytics property settings. That doesn’t mean that it’s not available, it means that the linking hasn’t been done yet. 

So, instead of starting in Google Analytics, the process begins in the Optimize interface. Under Settings, navigate to the Measurement section and edit. A dropdown will be available with a list of all the properties that you have access to. Unlike the previous version of Google Analytics, the integration links to a GA data stream instead of the GA property.

Once it’s linked, the icon will show up in Google Analytics:

When the link is active, Google Analytics 4 data can be used for audience targeting, conversion optimization, and objectives.

Note: If you are already linked to a legacy Google Analytics property, check with your team to make sure that it is ok to switch it to the Google Analytics 4 data.

Dig deeper: Is Google Analytics going away? What marketers need to know

Marketing integrations for media platforms

With the integration of BigQuery, Search Console, and Optimize, anyone can advance their analytics capabilities for current or future initiatives.

Below are brief explanations of the media platforms that Google Analytics 4 can integrate with. Most of these depend on what products are in use, what vertical an organization falls under, or other specific contexts and devices. 

Google Ads

What is Google Ads? It’s the most popular and well-known search advertising tool, formerly known as AdWords.

Who is it for? It’s for marketers, advertisers and paid media specialists.

What it does. Google Ads was one of the first products to have GA4 linking capabilities. It’s built to provide value both ways – by getting Ads metrics and reporting from Google Ads to GA and by sending audiences and getting conversions from GA to Google Ads.

Google Analytics 4 to Google Ads linking information and instructions here.

Display & Video 360

What is DV360? It’s a programmatic advertising platform. Also referred to as a DSP, DV360 is used to bid on display ad placements on publisher/content sites.

Who is it for? It’s for marketers, advertisers and paid media specialists within enterprise organizations.

Google Analytics 4 to DV360 linking information and instructions here.

Search Ads 360

What is SA360? This is like Google Ads, but super-charged. It’s a management and bidding tool to run ads across multiple channels and search engines.

Who is it for? It’s for marketers, advertisers and paid media specialists within enterprise organizations.

Google Analytics 4 to SA360 linking information and instructions here.

Google Ads Manager 

What is GAM? It’s an enterprise platform for publishers to manage and serve ads on their site or app.

Who is it for? Marketers, advertisers and paid media specialists within enterprise organizations.

Google Analytics 4 to GAM linking information and instructions here.

Google Merchant Center

What is Google Merchant Center? A separate platform from Google Ads to promote products, mainly on Google Shopping.

Who is it for? It’s for marketers and advertisers within an ecommerce organization.

Google Analytics 4 to Google Merchant Center linking information and instructions here.

Salesforce Marketing Cloud

SFMC is for cross-channel digital marketers. This integration is meant for use in the SFMC Journey Builder and can bring in Google Analytics data.

Google Analytics 4 to SFMC information and instructions here (through Salesforce).

Google Play

Google Play is Google’s app store and it’s for digital marketers who analyze in-app purchases and subscriptions.

Google Analytics 4 to Google Play linking information and instructions here.

If your organization is using any of those media tools, it’s a great time to start the strategy and process of leveraging Google Analytics 4 data to enhance analysis across multiple products and teams. There’s no reason not to start since they are available to all GA4 properties.

The post Using Google Analytics 4 integrations for insights and media activations appeared first on MarTech.

The biggest shake-up in the marketing analytics world is that Google Analytics as we know it is going to be sunset and will eventually stop collecting data in July 2023 (October 2023 for GA360 customers). There were mixed responses, to say the least – conflicting tweets, memes and disappointed forum posts were generally the first reactions to the news, and it proves that this drastic move needed to happen at some point. 

As more practitioners and marketers adopt the new Google Analytics 4 (GA4), the benefits are starting to flip the mood from nervousness to excitement.

The version of GA that’s been around for over a decade, Universal Analytics, is hard to leave behind since it’s such an embedded part of web measurement. GA4 was announced in October 2020 but wasn’t met with widespread eagerness that would be expected for a new, robust product. To be fair, there were quite a few other things going on in the world at that time, but in any case, marketers weren’t rushing to make the switch, and the industry seems to be going through the stages of grief for the familiar product:

• Denial – “I don’t need to change platforms, so I will ignore GA4 for now.”
• Anger – “How could Google get rid of Universal Analytics?”
• Bargaining – “What if the deadline is extended? Can we ask for more time?”
• Sadness – “It will take so much effort to migrate and learn a new tool.”
• Acceptance – “This is more advanced and helps with the cookieless future that I keep getting asked about. I’m in.”

It may be a challenging road ahead to migrate, but the move to Google Analytics 4 shouldn’t be considered bad news. It comes with new features, tracking methodology, a lower price point for 360 and has perks for users on the free version. The most important aspect is that it fits more appropriately into the current app landscape and is built to face compliance and privacy changes for what’s in effect and what’s coming.

Ultimately, GA4 is the solution to the internet now, not the internet from a decade ago.

Understanding the past, understanding the future

The news is disorienting, so the timeline should be put in perspective.

Google acquired a product in 2005 called Urchin. Before GA, web analytics was based on server log files, and it was not as intuitive or marketer-friendly. There are still relics of that era with things like UTM parameters (Urchin Tracking Modules) and the property IDs themselves. The “UA” in an ID like UA-12345-1 doesn’t represent Universal Analytics. It stands for Urchin Analytics. Since then, there have been new iterations of GA for the web. Here’s a list of where we’ve been:

• Urchin Analytics
• Google Analytics Synchronous Tracking
• Google Analytics Asynchronous Tracking
• Universal Analytics
• Global Site Tagging (gtag)

There’s one thing that these tracking methods from 2005 to 2022 have in common – all of them still process data and show up in reports, no matter which tracking library you’re using. So, Google is still processing data from the time when the internet looked like this. 

It’s been 10 years since the release of Universal Analytics. In 2012, Google Tag Manager had yet to be released, and mobile-first web design was a new concept. App tracking was still in beta, and it would be six years before DoubleClick products would evolve to become part of the new Google Marketing Platform. We’ve come a long way, so tracking had to be completely rebuilt, and the Google Analytics from 2005 to 2020 will be taken away and put on a shelf next to Google+ and Google Local.

The UA version of Google Analytics was designed to embrace multi-device behavior, collect more user data, and allow offline and cross-channel measurement. However, culture is no longer multi-screen – it’s multi-multi-multi-screen. The average number of connected devices per person in North America alone will reach 13 in 2023. Universal Analytics cannot easily track different platforms together, and it was not meant to do so. Now that we’re in a more app-centric phase of connectivity, GA4 is a better solution since it was built for that type of analysis. Instead of gathering more data, the goal is to use data that is modeled and as anonymous as possible.

Universal Analytics will be disappearing coincidentally around the same time as the death of the cookie. The hyperconnected landscape called for a necessary pivot for users to have more control over their data, more privacy considerations and more transparent analytics practice. Google Analytics 4 has answered that call with a variety of customizations and settings to establish trust with your visitors while continuing to activate on rich data. User tracking will now be supplemented with machine-learning data baked right into Google Analytics 4. Users’ current trends in behavior will be automatically analyzed to predict future behavior and provide modeled conversions. The privacy-centric features are a core component, but there are other reasons to embrace the change.

What to get excited about

In addition to being the first Google Analytics product to have the built-in capability to collect data from multiple sources, it is a better evolution for enterprise-level while also offering more to small- and mid-size businesses.

The free version of GA has turned into a freemium product. Standard non-paid users now have access (although limited) to tools like BigQuery, GMP integrations, more unsampled data, and access to advanced visualizations through Exploration Reports (formerly called Advanced Analysis).

For Google Analytics 360 customers, those features are much less limited, and some of the additional perks are:

• Enterprise-level data and user governance through roll-up and sub-properties.
• More control over data retention.
• Streaming and nearly unlimited BigQuery exports.
• Quicker processing, even for large data sets in the billions.
• The ability to use up to 400 advanced audiences to pass to marketing platforms.
• Unsampled custom reports, explorations, and the ability to use longer date ranges in advanced reports.
• Higher level of custom data collection for events, conversions, custom dimensions, and user properties.

Migrations were strongly suggested throughout these iterations but never forced (except for the Google Analytics app tracking SDK). However, older versions of tracking will not be as useful in 2023. It’s symbolic that even the echo of Urchin Analytics in those “UA-12345-1” properties is gone for good and replaced with Measurement IDs and data streams.

Deadlines and timelines

As a reminder, Universal Analytics will officially sunset in July 2023 for those on the free version and October 2023 for GA360 users. This means that properties will be read-only, and data sent to Google will not be processed. There won’t be exceptions, so migrating will be the top priority for everyone. Even if you’re not currently using the platform but have used it in the past, it’s still a time for action. We’re not just moving on. We’re also moving out – historical data will eventually be erased, so data must be saved and exported. The deletion won’t happen until at least six months after the sunset date, but it’s a crucial step in the migration process.

All web and app data should be 100% in Google Analytics 4 by the shutoff date, but ideally sooner. Parallel tracking should be in place and refined now so that data can be available on both platforms. The GA4 numbers won’t match 1:1 to Universal Analytics. Having year-over-year reports comparing UA to GA4 may be misleading, and reports will not be able to use the same data source. With GA4 tracking in parallel, next year’s reports will be comparing apples to apples. Depending on your organization, seasonality can guide how quickly to ramp up and set priorities for the most critical metrics and events. Whether it’s higher education enrollment, holiday ecommerce, or tax season, yearly activity is a consideration for building as much parity as possible between UA and GA4.

Next steps

The first step is to get GA4 on your websites and apps. It’s not too late to get started on a new strategy to fit the new tracking method and create your Google Analytics 4 properties, but delaying parallel tracking may cause reporting, remarketing, and compliance difficulties. After that, learning about how you can take advantage of the durable Google Analytics 4 should spark ideas and conversations beyond migration.

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